This notebook contains a set of analyses for analyzing ZeeGarcia’s boardgamegeek collection. The bulk of the analysis is focused on building a user-specific predictive model to predict the games that the specified user is likely to own. This enables us to ask questions like, based on the games the user currently owns, what games are a good fit for their collection? What upcoming games are they likely to purchase?
We can look at a basic description of the number of games that the user owns, has rated, has previously owned, etc.
What years has the user owned/rated games from? While we can’t see when a user added or removed a game from their collection, we can look at their collection by the years in which their games were published.
We can look at the most frequent types of categories, mechanics, designers, and artists that appear in a user’s collection.
We’ll examine predictive models trained on a user’s collection for games published through 2020. How many games has the user owned/rated/played in the training set (games prior to 2020)?
username | dataset | period | games_owned | games_rated |
ZeeGarcia | training | published before 2020 | 1,633 | 2,057 |
ZeeGarcia | validation | published 2020 | 59 | 86 |
ZeeGarcia | test | published after 2020 | 60 | 62 |
The main outcome we will be modeling for the user is owned, which refers to whether the user currently owns or has a previously owned a game in their collection. Our goal is to train a predictive model to learn the probability that a user will add a game to their collection based on its observable features. This amounts to looking at historical data and looking to find patterns that exist between features of games and games present in the user’s collection.
One of the models we trained was a decision tree, which looks for decision rules that can be used to separate games the user owns from games they don’t. The resulting model produces a decision corresponding to yes or no statements: to explain why the model predicts the user to own game, we start at the top of the tree and follow the rules that were learned from the training data.
Note: the tree below has been further pruned to make it easier to visualize.
Decision trees are highly interpretible models that are easy to train and can identify important interactions and nonlinearities present in the data. Individual trees have the drawback of being less predictive than other common models, but it can be useful to look at them to gain some understanding of key predictors and relationships found in the training data.
We can examine coefficients from another model we trained, which is a logistic regression with elastic net regularization (which I will refer to as a penalized logistic regression). Positive values indicate that a feature increases a user’s probability of owning/rating a game, while negative values indicate a feature decreases the probability. To be precise, the coefficients indicate the effect of a particular feature on the log-odds of a user owning a game.
Why did the model identify these features? We can make density plots of the important features for predicting whether the user owned a game. Blue indicates the density for games owned by the user, while grey indicates the density for games not owned by the user.
Binary predictors can be difficult to see with this visualization, so we can also directly examine the percentage of games in a user’s collection with a predictor vs the percentage of all games with that predictor.
% of Games with Feature | ||||
username | Feature | User_Collection | All_Games | Ratio |
ZeeGarcia | ZMan Games | 6.2% | 1.1% | 5.89 |
ZeeGarcia | Rio Grande Games | 7.0% | 1.5% | 4.72 |
ZeeGarcia | Asmodee | 9.2% | 2.1% | 4.45 |
ZeeGarcia | Fantasy Flight Games | 4.0% | 0.9% | 4.30 |
ZeeGarcia | Tricktaking | 5.0% | 1.5% | 3.36 |
ZeeGarcia | Games With Solitaire Rules | 9.4% | 4.9% | 1.93 |
ZeeGarcia | Card Game | 46.0% | 28.1% | 1.64 |
ZeeGarcia | Parker Brothers | 2.3% | 2.5% | 0.92 |
ZeeGarcia | Miniatures Game | 2.3% | 5.0% | 0.46 |
ZeeGarcia | Movies TV Radio Theme | 2.3% | 5.2% | 0.45 |
ZeeGarcia | Roll Spin And Move | 2.2% | 7.1% | 0.31 |
ZeeGarcia | Action Dexterity | 1.5% | 5.5% | 0.28 |
ZeeGarcia | Childrens Game | 1.7% | 8.5% | 0.20 |
ZeeGarcia | Wargame | 1.4% | 20.2% | 0.07 |
ZeeGarcia | Simulation | 0.8% | 10.9% | 0.07 |
ZeeGarcia | Movement Points | 0.2% | 2.5% | 0.07 |
Before predicting games in upcoming years, we can examine how well the model did and what games it liked in the training set. In this case, we used resampling techniques (cross validation) to ensure that the model had not seen a game before making its predictions.
Displaying the 100 games from the training set with the highest probability of ownership, highlighting in blue games the user has owned.
Rank | Published | ID | Name | Pr(Owned) | Owned |
1 | 2003 | 6068 | Queen's Necklace | 0.995 | yes |
2 | 2015 | 177639 | Raptor | 0.994 | yes |
3 | 2015 | 176920 | Mission: Red Planet (Second Edition) | 0.993 | yes |
4 | 2006 | 22141 | Cleopatra and the Society of Architects | 0.993 | yes |
5 | 2009 | 54998 | Cyclades | 0.991 | yes |
6 | 2016 | 200147 | Kanagawa | 0.991 | yes |
7 | 2014 | 155987 | Abyss | 0.990 | yes |
8 | 2005 | 15062 | Shadows over Camelot | 0.989 | yes |
9 | 2005 | 18258 | Mission: Red Planet | 0.982 | yes |
10 | 2016 | 205398 | Citadels | 0.981 | yes |
11 | 2015 | 173346 | 7 Wonders Duel | 0.980 | yes |
12 | 2014 | 157354 | Five Tribes | 0.979 | yes |
13 | 2019 | 251830 | Alhambra: Mega Box | 0.979 | yes |
14 | 2000 | 478 | Citadels | 0.978 | yes |
15 | 2019 | 265285 | Queenz: To Bee or Not to Bee | 0.976 | yes |
16 | 2007 | 29030 | Chicago Poker | 0.972 | yes |
17 | 2008 | 33107 | Senji | 0.969 | yes |
18 | 2018 | 239840 | Micropolis | 0.967 | yes |
19 | 2009 | 40793 | Dice Town | 0.966 | yes |
20 | 2019 | 286096 | Tapestry | 0.964 | no |
21 | 2011 | 70919 | Takenoko | 0.963 | yes |
22 | 2004 | 10997 | Boomtown | 0.961 | yes |
23 | 2017 | 197178 | DIG | 0.960 | yes |
24 | 2016 | 205610 | A Game of Thrones: Hand of the King | 0.957 | yes |
25 | 2014 | 148228 | Splendor | 0.956 | yes |
26 | 2006 | 24845 | Tomahawk | 0.954 | yes |
27 | 2014 | 154443 | Madame Ching | 0.953 | yes |
28 | 2012 | 129904 | Shadows over Camelot: The Card Game | 0.952 | no |
29 | 2016 | 193210 | Dice Stars | 0.950 | yes |
30 | 2009 | 40237 | Long Shot | 0.940 | yes |
31 | 2017 | 213893 | Yamataï | 0.939 | yes |
32 | 2012 | 116858 | Noah | 0.932 | yes |
33 | 2018 | 199792 | Everdell | 0.930 | yes |
34 | 2016 | 190639 | Zany Penguins | 0.925 | yes |
35 | 2018 | 259829 | Loser | 0.924 | no |
36 | 1997 | 42 | Tigris & Euphrates | 0.923 | yes |
37 | 2013 | 134453 | The Little Prince: Make Me a Planet | 0.922 | yes |
38 | 2015 | 158915 | GEM | 0.920 | yes |
39 | 2012 | 125311 | Okiya | 0.920 | yes |
40 | 2001 | 878 | Wyatt Earp | 0.920 | yes |
41 | 2019 | 281960 | Kingdomino Duel | 0.918 | yes |
42 | 2017 | 232043 | Queendomino | 0.915 | yes |
43 | 2011 | 103686 | Mundus Novus | 0.914 | yes |
44 | 2010 | 67185 | Sobek | 0.914 | yes |
45 | 2012 | 124742 | Android: Netrunner | 0.913 | yes |
46 | 2017 | 221107 | Pandemic Legacy: Season 2 | 0.910 | no |
47 | 2016 | 182120 | Histrio | 0.910 | yes |
48 | 2014 | 150926 | Roll Through the Ages: The Iron Age | 0.909 | yes |
49 | 2019 | 244191 | Naga Raja | 0.908 | yes |
50 | 2004 | 12942 | No Thanks! | 0.908 | yes |
51 | 2010 | 68448 | 7 Wonders | 0.906 | yes |
52 | 2004 | 9509 | Iglu Iglu | 0.904 | yes |
53 | 2000 | 823 | The Lord of the Rings | 0.903 | yes |
54 | 2014 | 165662 | Haru Ichiban | 0.902 | yes |
55 | 2016 | 204583 | Kingdomino | 0.902 | yes |
56 | 2019 | 270971 | Era: Medieval Age | 0.900 | no |
57 | 2016 | 201920 | Pocket Madness | 0.897 | yes |
58 | 2008 | 37380 | Roll Through the Ages: The Bronze Age | 0.893 | yes |
59 | 2008 | 34635 | Stone Age | 0.890 | no |
60 | 2004 | 14781 | Drôles de Zèbres | 0.887 | yes |
61 | 2013 | 143157 | SOS Titanic | 0.883 | yes |
62 | 2019 | 266192 | Wingspan | 0.883 | yes |
63 | 2004 | 9220 | Saboteur | 0.879 | yes |
64 | 2010 | 73439 | Troyes | 0.879 | no |
65 | 2013 | 148290 | Longhorn | 0.872 | yes |
66 | 2007 | 28023 | Jamaica | 0.871 | yes |
67 | 2017 | 192827 | RUM | 0.871 | yes |
68 | 1995 | 915 | Mystery of the Abbey | 0.868 | yes |
69 | 2019 | 285984 | Last Bastion | 0.867 | yes |
70 | 2018 | 260428 | Pandemic: Fall of Rome | 0.863 | yes |
71 | 2013 | 145645 | Le Fantôme de l'Opéra | 0.860 | yes |
72 | 2009 | 55427 | Mr. Jack in New York | 0.858 | yes |
73 | 2018 | 244330 | Scarabya | 0.856 | yes |
74 | 2013 | 143693 | Glass Road | 0.856 | no |
75 | 2005 | 18588 | Les Fils de Samarande | 0.855 | no |
76 | 2011 | 108783 | Dr. Shark | 0.855 | no |
77 | 2004 | 10682 | Atlas & Zeus | 0.853 | yes |
78 | 2016 | 197893 | Crazy Mistigri | 0.853 | no |
79 | 2005 | 18289 | Key Largo | 0.853 | yes |
80 | 2019 | 276042 | Conspiracy: Abyss Universe | 0.852 | yes |
81 | 2004 | 9216 | Goa | 0.847 | yes |
82 | 2018 | 233080 | Book of Dragons | 0.846 | no |
83 | 2006 | 21763 | Mr. Jack | 0.844 | yes |
84 | 2010 | 68182 | Isla Dorada | 0.842 | yes |
85 | 2014 | 154600 | Desperados of Dice Town | 0.841 | yes |
86 | 2011 | 91523 | Mondo | 0.835 | no |
87 | 2017 | 195454 | Nut | 0.833 | yes |
88 | 1998 | 503 | Through the Desert | 0.833 | yes |
89 | 2011 | 100423 | Elder Sign | 0.831 | yes |
90 | 2007 | 28738 | Kamon | 0.826 | yes |
91 | 2009 | 45134 | Arcana | 0.825 | yes |
92 | 2017 | 201825 | Ex Libris | 0.825 | yes |
93 | 2006 | 21654 | Iliad | 0.824 | yes |
94 | 2017 | 200847 | Secrets | 0.823 | no |
95 | 2017 | 195373 | BOO | 0.821 | no |
96 | 2016 | 160010 | Conan | 0.821 | yes |
97 | 2019 | 285774 | Marvel Champions: The Card Game | 0.821 | yes |
98 | 1995 | 112 | Condottiere | 0.820 | yes |
99 | 2007 | 28143 | Race for the Galaxy | 0.819 | yes |
100 | 2012 | 129622 | Love Letter | 0.816 | yes |
This section contains a variety of visualizations and metrics for assessing the performance of the model(s) during resampling. If you’re not particularly interested in predictive modeling, skip down further to the predictions from the model.
An easy way to examine the performance of classification model is to view a separation plot. We plot the predicted probabilities from the model for every game (from resampling) from lowest to highest. We then overlay a blue line for any game that the user does own. A good classifier is one that is able to separate the blue (games owned by the user) from the white (games not owned by the user), with most of the blue occurring at the highest probabilities (right side of the chart).
We can more formally assess how well each model did in resampling by looking at the area under the receiver operating characteristic curve. A perfect model would receive a score of 1, while a model that cannot predict the outcome will default to a score of 0.5. The extent to which something is a good score depends on the setting, but generally anything in the .8 to .9 range is very good while the .7 to .8 range is perfectly acceptable.
wflow_id | .metric | .estimator | .estimate |
GLM | roc_auc | binary | 0.84 |
Decision Tree | roc_auc | binary | 0.77 |
Another way to think about the model performance is to view its lift, or its ability to detect the positive outcomes over that of a null model. High lift indicates the model can much more quickly find all of the positive outcomes (in this case, games owned or played by the user), while a model with no lift is no better than random guessing. A gains chart is another way to view this.
While we are probably more interested in the lift provided by the models to evaluate their efficacy, we can also explore the optimal cutpoint if we wanted to define a hard threshold for identifying games a user will own vs not own.
The threshold we select depends on how we much we care about false positives (games the model predicts that the user does not own) vs false negatives (games the user owns that the model does not predict). We can toggle threshold to
Finally, we can understand the performance of the model by examining its calibration. If the model assigns a probability of 5%, how often does the outcome actually occur? A well calibrated model is one in which the predicted probabilities reflect the probabilities we would observe in the actual data. We can assess the calibration of a model by grouping its predictions into bins and assessing how often we observe the outcome versus how often our model expects to observe the outcome.
A model that is well calibrated will closely follow the dashed line - its expected probabilities match that of the observed probabilities. A model that consistently underestimates the probability of the event will be over this dashed line, be a while a model that overestimates the probability will be under the dashed line.
What games does the model think ZeeGarcia is most likely to own that are not in their collection?
Published | ID | Name | Pr(Owned) | Owned |
2019 | 286096 | Tapestry | 0.964 | no |
2012 | 129904 | Shadows over Camelot: The Card Game | 0.952 | no |
2018 | 259829 | Loser | 0.924 | no |
2017 | 221107 | Pandemic Legacy: Season 2 | 0.910 | no |
2019 | 270971 | Era: Medieval Age | 0.900 | no |
What games does the model think ZeeGarcia is least likely to own that are in their collection?
Published | ID | Name | Pr(Owned) | Owned |
2015 | 174155 | Lignum | 0.001 | yes |
2014 | 165401 | Wir sind das Volk! | 0.002 | yes |
2002 | 11873 | AMC Reel Clues | 0.003 | yes |
2006 | 25758 | Trüffel-Schnüffel | 0.003 | yes |
2007 | 28843 | 300: The Board Game | 0.003 | yes |
Top 25 games most likely to be owned by the user in each year, highlighting in blue the games that the user has owned.
rank | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 |
1 | Shadows over Camelot: The Card Game | The Little Prince: Make Me a Planet | Abyss | Raptor | Kanagawa | DIG | Micropolis | Alhambra: Mega Box |
2 | Noah | SOS Titanic | Five Tribes | Mission: Red Planet (Second Edition) | Citadels | Yamataï | Everdell | Queenz: To Bee or Not to Bee |
3 | Okiya | Longhorn | Splendor | 7 Wonders Duel | A Game of Thrones: Hand of the King | Queendomino | Loser | Tapestry |
4 | Android: Netrunner | Le Fantôme de l'Opéra | Madame Ching | GEM | Dice Stars | Pandemic Legacy: Season 2 | Pandemic: Fall of Rome | Kingdomino Duel |
5 | Love Letter | Glass Road | Roll Through the Ages: The Iron Age | 504 | Zany Penguins | RUM | Scarabya | Naga Raja |
6 | Button Up! | Cinque Terre | Haru Ichiban | LIE | Histrio | Nut | Book of Dragons | Era: Medieval Age |
7 | Sky Tango | Pentos | Desperados of Dice Town | Sylvion | Kingdomino | Ex Libris | Imaginarium | Wingspan |
8 | Tokaido | Room 25 | Imperial Settlers | Mysterium | Pocket Madness | Secrets | Jurassic Snack | Last Bastion |
9 | Agricola: All Creatures Big and Small | Cappuccino | Roll for the Galaxy | Between Two Cities | Crazy Mistigri | BOO | Kiwara | Conspiracy: Abyss Universe |
10 | Robinson Crusoe: Adventures on the Cursed Island | Asante | Onirim (Second Edition) | Pandemic Legacy: Season 1 | Conan | BOX | Greedy Kingdoms | Marvel Champions: The Card Game |
11 | Divinare | Euphoria: Build a Better Dystopia | Patchwork | SHH | HMS Dolores | SOW | Arkham Horror (Third Edition) | Ice Team |
12 | Zooloretto: The Dice Game | Bruges | Pandemic: The Cure | Viticulture Essential Edition | Arkham Horror: The Card Game | ORC | Fist of Dragonstones: The Tavern Edition | Ishtar: Gardens of Babylon |
13 | Descent: Journeys in the Dark (Second Edition) | Sheepzzz | Dragon Run | The Little Prince: Rising to the Stars | Bloodborne: The Card Game | Miaui | Hokkaido | Tiny Towns |
14 | Ginkgopolis | Legacy: The Testament of Duke de Crecy | Roll Through the Ages: The Iron Age with Mediterranean Expansion | HUE | Pandemic: Reign of Cthulhu | Santa Maria | KeyForge: Call of the Archons | The Magnificent |
15 | Targi | Gravwell: Escape from the 9th Dimension | Chimera | Arboretum | The Castles of Burgundy: The Card Game | GYM | Rebel Nox | Trails of Tucana |
16 | Think Again! | Mascarade | Akrotiri | TKO | Archaeology: The New Expedition | Legend of the Five Rings: The Card Game | Duelosaur Island | Herbaceous Sprouts |
17 | Escape: The Curse of the Temple | Scotland Yard Master | VivaJava: The Coffee Game: The Dice Game | SteamRollers | Covert | WOO | New Frontiers | KeyForge: Age of Ascension |
18 | Il Vecchio | Terror in Meeple City | Age of War | Blood Rage | Explorers of the North Sea | A Column of Fire | Underwater Cities | Victorian Masterminds |
19 | Antartik | The Builders: Middle Ages | Blue Moon Legends | BUS | Smash Up: Cease and Desist | SPY | Lords of Hellas | Run Fight or Die: Reloaded |
20 | Urbion | The Ravens of Thri Sahashri | DungeonQuest Revised Edition | The Game | Honshū | Oliver Twist | The Pirate Republic | Pandemic: Rapid Response |
21 | Africana | BodgerMania | Linko! | Mondo: Der rasante Legespaß | Agricola (Revised Edition) | Majesty: For the Realm | Holmes and Moriarty | Noctiluca |
22 | Zombicide | Tash-Kalar: Arena of Legends | Nations: The Dice Game | Mogul | Love Letter Premium | Jump Drive | Architects of the West Kingdom | Machi Koro Legacy |
23 | The Hobbit Card Game | Forbidden Desert | Saboteur: The Duel | Bastion | Star Wars: Destiny | The Castles of Burgundy: The Dice Game | Cosmic Run: Regeneration | Amul |
24 | Ohne Furcht und Adel | Kronen für den König | Till Dawn | Plums | Heir to the Pharaoh | Azul | Treasure Island | Sierra West |
25 | Thunderstone Advance: Towers of Ruin | Bruxelles 1893 | Dragon's Hoard | Valley of the Kings: Afterlife | The Butterfly Garden | Zooloretto Duell | Dragons | Silver & Gold |
This is an interactive table for the model’s predictions for the training set (from resampling).
We’ll validate the model by looking at its predictions for games published in 2020. That is, how well did a model trained on a user’s collection through 2020 perform in predicting games for the user in 2020?
username | outcome | dataset | method | .metric | .estimate |
ZeeGarcia | owned | validation | GLM | roc_auc | 0.756 |
ZeeGarcia | owned | validation | Decision Tree | roc_auc | 0.665 |
Table of top 50 games from 2020, highlighting games that the user owns.
Published | ID | Name | Pr(Owned) | Owned |
2020 | 265784 | Cleopatra and the Society of Architects: Deluxe Edition | 0.951 | yes |
2020 | 297661 | Gold River | 0.936 | yes |
2020 | 323262 | Velonimo | 0.935 | yes |
2020 | 229782 | Roland Wright: The Dice Game | 0.928 | no |
2020 | 316377 | 7 Wonders (Second Edition) | 0.885 | yes |
2020 | 314040 | Pandemic Legacy: Season 0 | 0.872 | yes |
2020 | 292917 | Mosquito Show | 0.832 | no |
2020 | 297666 | Jurassic Brunch | 0.792 | yes |
2020 | 303672 | Trek 12: Himalaya | 0.753 | yes |
2020 | 288169 | The Fox in the Forest Duet | 0.728 | no |
2020 | 283155 | Calico | 0.643 | no |
2020 | 291457 | Gloomhaven: Jaws of the Lion | 0.636 | yes |
2020 | 298572 | Cosmic Encounter Duel | 0.629 | no |
2020 | 301919 | Pandemic: Hot Zone – North America | 0.617 | yes |
2020 | 293678 | Stellar | 0.605 | no |
2020 | 300010 | Dragomino | 0.600 | no |
2020 | 293556 | Gloomy Graves | 0.595 | no |
2020 | 301880 | Raiders of Scythia | 0.576 | no |
2020 | 318983 | Faiyum | 0.554 | no |
2020 | 233262 | Tidal Blades: Heroes of the Reef | 0.502 | no |
2020 | 267009 | Rome & Roll | 0.498 | no |
2020 | 262208 | Dungeon Drop | 0.498 | no |
2020 | 270109 | Iwari | 0.497 | yes |
2020 | 245659 | Vampire: The Masquerade – Vendetta | 0.493 | no |
2020 | 295486 | My City | 0.482 | no |
2020 | 298371 | Wild Space | 0.472 | no |
2020 | 312804 | Pendulum | 0.461 | no |
2020 | 325635 | Unmatched: Little Red Riding Hood vs. Beowulf | 0.459 | no |
2020 | 306481 | Tawantinsuyu: The Inca Empire | 0.458 | no |
2020 | 294484 | Unmatched: Cobble & Fog | 0.454 | no |
2020 | 301607 | KeyForge: Mass Mutation | 0.442 | no |
2020 | 307844 | Atheneum: Mystic Library | 0.441 | no |
2020 | 304285 | Infinity Gauntlet: A Love Letter Game | 0.430 | yes |
2020 | 313698 | Monster Expedition | 0.423 | no |
2020 | 301399 | Lyttle Wood | 0.420 | yes |
2020 | 303054 | Yacht Rock | 0.419 | no |
2020 | 296512 | The Game: Quick & Easy | 0.409 | yes |
2020 | 302310 | Nanaki | 0.407 | no |
2020 | 300531 | Paleo | 0.402 | yes |
2020 | 184267 | On Mars | 0.401 | no |
2020 | 293014 | Nidavellir | 0.398 | no |
2020 | 293309 | Kraken Attack! | 0.397 | no |
2020 | 296667 | Vintage | 0.394 | no |
2020 | 309113 | Ticket to Ride: Amsterdam | 0.391 | no |
2020 | 284777 | Unmatched: Jurassic Park – InGen vs Raptors | 0.389 | no |
2020 | 299592 | Beez | 0.385 | no |
2020 | 313531 | Rustling Leaves | 0.383 | no |
2020 | 298047 | Marvel United | 0.382 | yes |
2020 | 324345 | キャットインザボックス (Cat in the box) | 0.382 | no |
2020 | 294232 | Stolen Paintings | 0.381 | no |
We can then refit our model to the training and validation set in order to predict all upcoming games for the user.
Examine the top 100 upcoming games, highlighting in blue ones the user already owns.
Published | ID | Name | Pr(Owned) | Owned |
2021 | 332944 | Sobek: 2 Players | 0.957 | yes |
2022 | 349067 | The Lord of the Rings: The Card Game – Revised Core Set | 0.806 | no |
2021 | 334644 | Nicodemus | 0.801 | yes |
2021 | 329670 | Pandemic: Hot Zone – Europe | 0.778 | no |
2021 | 344415 | Trek 12: Amazonia | 0.697 | no |
2021 | 329714 | Dreadful Circus | 0.686 | no |
2022 | 295374 | Long Shot: The Dice Game | 0.681 | yes |
2021 | 303676 | Oh My Brain | 0.678 | yes |
2021 | 290236 | Canvas | 0.667 | no |
2021 | 340041 | Kingdomino Origins | 0.631 | yes |
2021 | 328535 | Mandragora | 0.616 | yes |
2021 | 340466 | Unfathomable | 0.611 | no |
2021 | 331635 | Kameloot | 0.603 | no |
2021 | 285967 | Ankh: Gods of Egypt | 0.599 | yes |
2021 | 314491 | Meadow | 0.589 | no |
2021 | 340237 | Wonder Book | 0.583 | no |
2021 | 304783 | Hadrian's Wall | 0.581 | no |
2021 | 339906 | The Hunger | 0.571 | no |
2021 | 342848 | World of Warcraft: Wrath of the Lich King | 0.549 | no |
2021 | 340834 | Gravwell: 2nd Edition | 0.541 | no |
2021 | 339789 | Welcome to the Moon | 0.534 | no |
2021 | 315937 | X-Men: Mutant Insurrection | 0.528 | no |
2021 | 340677 | Bad Company | 0.524 | no |
2022 | 332393 | Bridge City Poker | 0.506 | no |
2021 | 341358 | INSERT | 0.497 | no |
2022 | 338364 | Pumafiosi | 0.494 | no |
2021 | 324242 | Sheepy Time | 0.493 | no |
2022 | 356033 | Libertalia: Winds of Galecrest | 0.482 | no |
2022 | 304051 | Creature Comforts | 0.477 | no |
2021 | 316080 | KeyForge: Dark Tidings | 0.442 | no |
2021 | 344408 | Full Throttle! | 0.440 | no |
2021 | 339905 | Love Letter: Princess Princess Ever After | 0.435 | no |
2021 | 329465 | Red Rising | 0.426 | yes |
2021 | 329529 | Magellan: Elcano | 0.419 | no |
2022 | 349793 | Age of Rome | 0.419 | no |
2021 | 333553 | For the King (and Me) | 0.418 | no |
2021 | 329084 | Space Dragons | 0.417 | no |
2022 | 308028 | Drop Drive | 0.408 | no |
2021 | 344258 | That Time You Killed Me | 0.395 | no |
2021 | 336382 | Marvel United: X-Men | 0.393 | no |
2022 | 356996 | The Border | 0.393 | no |
2021 | 310198 | Escape: Roll & Write | 0.383 | no |
2021 | 335541 | We Care: a Grizzled Game | 0.379 | no |
2022 | 275215 | Namiji | 0.378 | no |
2021 | 322014 | All-Star Draft | 0.375 | no |
2021 | 346703 | 7 Wonders: Architects | 0.369 | no |
2021 | 316343 | Capital Lux 2: Pocket | 0.362 | no |
2021 | 324856 | The Crew: Mission Deep Sea | 0.358 | no |
2021 | 313262 | Shamans | 0.354 | no |
2022 | 338460 | The Isle of Cats: Explore & Draw | 0.352 | no |
2021 | 333055 | Subastral | 0.342 | no |
2021 | 331549 | MiniQuest Adventures | 0.339 | no |
2022 | 335764 | Unmatched: Battle of Legends, Volume Two | 0.335 | no |
2021 | 339790 | Cocktail | 0.329 | no |
2021 | 346553 | Heuschrecken Poker | 0.319 | no |
2021 | 286667 | Tutankhamun | 0.316 | no |
2021 | 305682 | Rolling Realms | 0.313 | no |
2021 | 313730 | Harsh Shadows | 0.311 | no |
2021 | 281248 | Cape May | 0.309 | no |
2021 | 335678 | Let's Make a Bus Route: The Dice Game | 0.305 | yes |
2021 | 342073 | Berried Treasure | 0.304 | no |
2022 | 353765 | Awimbawé | 0.298 | no |
2022 | 353470 | Star Wars: Jabba's Palace – A Love Letter Game | 0.295 | no |
2021 | 330608 | Cryo | 0.292 | no |
2021 | 323156 | Stroganov | 0.290 | no |
2021 | 344405 | Cartaventura: Oklahoma | 0.284 | no |
2021 | 341048 | Free Ride | 0.281 | no |
2021 | 300523 | Biblios: Quill and Parchment | 0.281 | no |
2021 | 316287 | Quest | 0.279 | no |
2021 | 304324 | Dive | 0.278 | no |
2022 | 344839 | Dog Lover | 0.277 | yes |
2022 | 319910 | Pagan: Fate of Roanoke | 0.277 | no |
2022 | 275284 | Arkeis | 0.277 | no |
2021 | 311990 | Macaron | 0.276 | no |
2021 | 345036 | Qwixx Longo | 0.276 | no |
2021 | 283242 | The Whatnot Cabinet | 0.274 | no |
2021 | 298069 | Cubitos | 0.270 | no |
2021 | 292899 | Tribune | 0.269 | no |
2021 | 300305 | Nanga Parbat | 0.267 | no |
2021 | 328479 | Living Forest | 0.267 | no |
2021 | 314088 | Agropolis | 0.264 | no |
2021 | 295607 | Canopy | 0.264 | yes |
2021 | 322560 | Maeshowe: an Orkney Saga | 0.263 | no |
2021 | 331946 | Faux Diamonds | 0.262 | no |
2021 | 259962 | Stress Botics | 0.260 | no |
2021 | 295947 | Cascadia | 0.259 | yes |
2022 | 283137 | Human Punishment: The Beginning | 0.258 | no |
2022 | 275463 | Cactus Town | 0.257 | no |
2021 | 282776 | Tumble Town | 0.255 | no |
2021 | 257706 | Zoo-ography | 0.255 | no |
2021 | 343696 | Dune: Betrayal | 0.254 | no |
2022 | 310873 | Carnegie | 0.253 | no |
2022 | 340672 | Council of 12 | 0.251 | no |
2021 | 341362 | Reapers | 0.250 | no |
2021 | 315234 | Embarcadero | 0.249 | no |
2021 | 306881 | Railroad Ink Challenge: Lush Green Edition | 0.249 | no |
2021 | 306882 | Railroad Ink Challenge: Shining Yellow Edition | 0.249 | no |
2022 | 347702 | Las Vegan | 0.247 | no |
2022 | 288080 | Dice Realms | 0.247 | no |
2021 | 348461 | Castle Break | 0.246 | no |